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Abstract When species simultaneously compete with two or more species of competitor, higher‐order interactions (HOIs) can lead to emergent properties not present when species interact in isolated pairs. To extend ecological theory to multi‐competitor communities, ecologists must confront the challenges of measuring and interpreting HOIs in models of competition fit to data from nature. Such efforts are hindered by the fact that different studies use different definitions, and these definitions have unclear relationships to one another. Here, we propose a distinction between ‘soft’ HOIs, which identify possible interaction modification by competitors, and ‘hard’ HOIs, which identify interactions uniquely emerging in systems with three or more competitors. We show how these two classes of HOI differ in their motivation and interpretation, as well as the tests one uses to identify them in models fit to data. We then show how to operationalise this structure of definitions by analysing the results of a simulated competition experiment underlain by a consumer resource model. In the course of doing so, we clarify the challenges of interpreting HOIs in nature, and suggest a more precise framing of this research endeavour to catalyse further investigations. 

Abstract Spatial synchrony may be tail‐dependent, that is, stronger when populations are abundant than scarce, or vice‐versa. Here, ‘tail‐dependent’ follows from distributions having a lower tail consisting of relatively low values and an upper tail of relatively high values. We present a general theory of how the distribution and correlation structure of an environmental driver translates into tail‐dependent spatial synchrony through a non‐linear response, and examine empirical evidence for theoretical predictions in giant kelp along the California coastline. In sheltered areas, kelp declines synchronously (lower‐tail dependence) when waves are relatively intense, because waves below a certain height do little damage to kelp. Conversely, in exposed areas, kelp is synchronised primarily by periods of calmness that cause shared recovery (upper‐tail dependence). We find evidence for geographies of tail dependence in synchrony, which helps structure regional population resilience: areas where population declines are asynchronous may be more resilient to disturbance because remnant populations facilitate reestablishment. 

Abstract The daunting complexity of ecosystems has led ecologists to use mathematical modelling to gain understanding of ecological relationships, processes and dynamics. In pursuit of mathematical tractability, these models use simplified descriptions of key patterns, processes and relationships observed in nature. In contrast, ecological data are often complex, scale‐dependent, space‐time correlated, and governed by nonlinear relations between organisms and their environment. This disparity in complexity between ecosystem models and data has created a large gap in ecology between model and data‐driven approaches. Here, we explore data assimilation (DA) with the Ensemble Kalman filter to fuse a two‐predator‐two‐prey model with abundance data from a 2600+ day experiment of a plankton community. We analyse how frequently we must assimilate measured abundances to predict accurately population dynamics, and benchmark our population model's forecast horizon against a simple null model. Results demonstrate that DA enhances the predictability and forecast horizon of complex community dynamics.